Methods and devices for programming a state machine engine

ABSTRACT

A state machine engine having a program buffer. The program buffer is configured to receive configuration data via a bus interface for configuring a state machine lattice. The state machine engine also includes a repair map buffer configured to provide repair map data to an external device via the bus interface. The state machine lattice includes multiple programmable elements. Each programmable element includes multiple memory cells configured to analyze data and to output a result of the analysis.

BACKGROUND

1. Field of Invention

Embodiments of the invention relate generally to electronic devices and,more specifically, in certain embodiments, to electronic devices withparallel devices for data analysis.

2. Description of Related Art

Complex data analysis (e.g., pattern recognition) can be inefficient toperform on a conventional von Neumann based computer. A biologicalbrain, in particular a human brain, however, is adept at performingcomplex data analysis. Current research suggests that a human brainperforms data analysis using a series of hierarchically organized neuronlayers in the neocortex. Neurons in the lower layers of the hierarchyanalyze “raw signals” from, for example, sensory organs, while neuronsin higher layers analyze signal outputs from neurons in the lowerlevels. This hierarchical system in the neocortex, possibly incombination with other areas of the brain, accomplishes the complex dataanalysis that enables humans to perform high level functions such asspatial reasoning, conscious thought, and complex language.

In the field of computing, pattern recognition tasks, for example, areincreasingly challenging. Ever larger volumes of data are transmittedbetween computers, and the number of patterns that users wish to detectis increasing. For example, spam or malware are often detected bysearching for patterns in a data stream, e.g., particular phrases orpieces of code. The number of patterns increases with the variety ofspam and malware, as new patterns may be implemented to search for newvariants. Searching a data stream for each of these patterns can form acomputing bottleneck. Often, as the data stream is received, it issearched for each pattern, one at a time. The delay before the system isready to search the next portion of the data stream increases with thenumber of patterns. Thus, pattern recognition may slow the receipt ofdata.

Hardware has been designed to search a data stream for patterns, butthis hardware often is unable to process adequate amounts of data in anamount of time given. Some devices configured to search a data stream doso by distributing the data stream among a plurality of circuits. Thecircuits each determine whether the data stream matches a portion of apattern. Often, a large number of circuits operate in parallel, eachsearching the data stream at generally the same time. However, there hasnot been a system that effectively allows for performing complex dataanalysis in a manner more comparable to that of a biological brain.Development of such a system is desirable.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example of system having a state machine engine,according to various embodiments of the invention.

FIG. 2 illustrates an example of an finite state machine (FSM) latticeof the state machine engine of FIG. 1, according to various embodimentsof the invention.

FIG. 3 illustrates an example of a block of the FSM lattice of FIG. 2,according to various embodiments of the invention.

FIG. 4 illustrates an example of a row of the block of FIG. 3, accordingto various embodiments of the invention.

FIG. 5 illustrates an example of a Group of Two of the row of FIG. 4,according to various embodiments of the invention.

FIG. 6 illustrates an example of a finite state machine graph, accordingto various embodiments of the invention.

FIG. 7 illustrates an example of two-level hierarchy implemented withFSM lattices, according to various embodiments of the invention.

FIG. 8 illustrates an example of a method for a compiler to convertsource code into a binary file for programming of the FSM lattice ofFIG. 2, according to various embodiments of the invention.

FIG. 9 illustrates a state machine engine, according to variousembodiments of the invention.

FIG. 10 illustrates a flow chart showing a method for programming a FSMlattice in a state machine engine, according to various embodiments ofthe invention.

DETAILED DESCRIPTION

Turning now to the figures, FIG. 1 illustrates an embodiment of aprocessor-based system, generally designated by reference numeral 10.The system 10 (e.g., data analysis system) may be any of a variety oftypes such as a desktop computer, laptop computer, pager, cellularphone, personal organizer, portable audio player, control circuit,camera, etc. The system 10 may also be a network node, such as a router,a server, or a client (e.g., one of the previously-described types ofcomputers). The system 10 may be some other sort of electronic device,such as a copier, a scanner, a printer, a game console, a television, aset-top video distribution or recording system, a cable box, a personaldigital media player, a factory automation system, an automotivecomputer system, or a medical device. (The terms used to describe thesevarious examples of systems, like many of the other terms used herein,may share some referents and, as such, should not be construed narrowlyin virtue of the other items listed.)

In a typical processor-based device, such as the system 10, a processor12, such as a microprocessor, controls the processing of systemfunctions and requests in the system 10. Further, the processor 12 maycomprise a plurality of processors that share system control. Theprocessor 12 may be coupled directly or indirectly to each of theelements in the system 10, such that the processor 12 controls thesystem 10 by executing instructions that may be stored within the system10 or external to the system 10.

In accordance with the embodiments described herein, the system 10includes a state machine engine 14, which may operate under control ofthe processor 12. As used herein, the state machine engine 14 refers toa single device (e.g., single chip). The state machine engine 14 mayemploy any automaton theory. For example, the state machine engine 14may employ one of a number of state machine architectures, including,but not limited to Mealy architectures, Moore architectures, FiniteState Machines (FSMs), Deterministic FSMs (DFSMs), Bit-Parallel StateMachines (BPSMs), etc. Though a variety of architectures may be used,for discussion purposes, the application refers to FSMs. However, thoseskilled in the art will appreciate that the described techniques may beemployed using any one of a variety of state machine architectures.

As discussed further below, the state machine engine 14 may include anumber of (e.g., one or more) finite state machine (FSM) lattices (e.g.,core of a chip). For purposes of this application the term “lattice”refers to an organized framework (e.g., routing matrix, routing network,frame) of elements (e.g., Boolean cells, counter cells, state machineelements, state transition elements). Furthermore, the “lattice” mayhave any suitable shape, structure, or hierarchical organization (e.g.,grid, cube, spherical, cascading). Each FSM lattice may implementmultiple FSMs that each receive and analyze the same data in parallel.Further, the FSM lattices may be arranged in groups (e.g., clusters),such that clusters of FSM lattices may analyze the same input data inparallel. Further, clusters of FSM lattices of the state machine engine14 may be arranged in a hierarchical structure wherein outputs fromstate machine lattices on a lower level of the hierarchical structuremay be used as inputs to state machine lattices on a higher level. Bycascading clusters of parallel FSM lattices of the state machine engine14 in series through the hierarchical structure, increasingly complexpatterns may be analyzed (e.g., evaluated, searched, etc.).

Further, based on the hierarchical parallel configuration of the statemachine engine 14, the state machine engine 14 can be employed forcomplex data analysis (e.g., pattern recognition) in systems thatutilize high processing speeds. For instance, embodiments describedherein may be incorporated in systems with processing speeds of 1GByte/sec. Accordingly, utilizing the state machine engine 14, data fromhigh speed memory devices or other external devices may be rapidlyanalyzed. The state machine engine 14 may analyze a data streamaccording to several criteria (e.g., search terms), at about the sametime, e.g., during a single device cycle. Each of the FSM latticeswithin a cluster of FSMs on a level of the state machine engine 14 mayeach receive the same search term from the data stream at about the sametime, and each of the parallel FSM lattices may determine whether theterm advances the state machine engine 14 to the next state in theprocessing criterion. The state machine engine 14 may analyze termsaccording to a relatively large number of criteria, e.g., more than 100,more than 110, or more than 10,000. Because they operate in parallel,they may apply the criteria to a data stream having a relatively highbandwidth, e.g., a data stream of greater than or generally equal to 1GByte/sec, without slowing the data stream.

In one embodiment, the state machine engine 14 may be configured torecognize (e.g., detect) a great number of patterns in a data stream.For instance, the state machine engine 14 may be utilized to detect apattern in one or more of a variety of types of data streams that a useror other entity might wish to analyze. For example, the state machineengine 14 may be configured to analyze a stream of data received over anetwork, such as packets received over the Internet or voice or datareceived over a cellular network. In one example, the state machineengine 14 may be configured to analyze a data stream for spam ormalware. The data stream may be received as a serial data stream, inwhich the data is received in an order that has meaning, such as in atemporally, lexically, or semantically significant order. Alternatively,the data stream may be received in parallel or out of order and, then,converted into a serial data stream, e.g., by reordering packetsreceived over the Internet. In some embodiments, the data stream maypresent terms serially, but the bits expressing each of the terms may bereceived in parallel. The data stream may be received from a sourceexternal to the system 10, or may be formed by interrogating a memorydevice, such as the memory 16, and forming the data stream from datastored in the memory 16. In other examples, the state machine engine 14may be configured to recognize a sequence of characters that spell acertain word, a sequence of genetic base pairs that specify a gene, asequence of bits in a picture or video file that form a portion of animage, a sequence of bits in an executable file that form a part of aprogram, or a sequence of bits in an audio file that form a part of asong or a spoken phrase. The stream of data to be analyzed may includemultiple bits of data in a binary format or other formats, e.g., baseten, ASCII, etc. The stream may encode the data with a single digit ormultiple digits, e.g., several binary digits.

As will be appreciated, the system 10 may include memory 16. The memory16 may include volatile memory, such as Dynamic Random Access Memory(DRAM), Static Random Access Memory (SRAM), Synchronous DRAM (SDRAM),Double Data Rate DRAM (DDR SDRAM), DDR2 SDRAM, DDR3 SDRAM, etc. Thememory 16 may also include non-volatile memory, such as read-only memory(ROM), PC-RAM, silicon-oxide-nitride-oxide-silicon (SONOS) memory,metal-oxide-nitride-oxide-silicon (MONOS) memory, polysilicon floatinggate based memory, and/or other types of flash memory of variousarchitectures (e.g., NAND memory, NOR memory, etc.) to be used inconjunction with the volatile memory. The memory 16 may include one ormore memory devices, such as DRAM devices, that may provide data to beanalyzed by the state machine engine 14. As used herein, the term“provide” may generically refer to direct, input, insert, send,transfer, transmit, generate, give, output, place, write, etc. Suchdevices may be referred to as or include solid state drives (SSD's),MultimediaMediaCards (MMC's), SecureDigital (SD) cards, CompactFlash(CF) cards, or any other suitable device. Further, it should beappreciated that such devices may couple to the system 10 via anysuitable interface, such as Universal Serial Bus (USB), PeripheralComponent Interconnect (PCI), PCI Express (PCI-E), Small Computer SystemInterface (SCSI), IEEE 1394 (Firewire), or any other suitable interface.To facilitate operation of the memory 16, such as the flash memorydevices, the system 10 may include a memory controller (notillustrated). As will be appreciated, the memory controller may be anindependent device or it may be integral with the processor 12.Additionally, the system 10 may include an external storage 18, such asa magnetic storage device. The external storage may also provide inputdata to the state machine engine 14.

The system 10 may include a number of additional elements. For instance,a compiler 20 may be used to configure (e.g., program) the state machineengine 14, as described in more detail with regard to FIG. 8. An inputdevice 22 may also be coupled to the processor 12 to allow a user toinput data into the system 10. For instance, an input device 22 may beused to input data into the memory 16 for later analysis by the statemachine engine 14. The input device 22 may include buttons, switchingelements, a keyboard, a light pen, a stylus, a mouse, and/or a voicerecognition system, for instance. An output device 24, such as a displaymay also be coupled to the processor 12. The display 24 may include anLCD, a CRT, LEDs, and/or an audio display, for example. They system mayalso include a network interface device 26, such as a Network InterfaceCard (NIC), for interfacing with a network, such as the Internet. Aswill be appreciated, the system 10 may include many other components,depending on the application of the system 10.

FIGS. 2-5 illustrate an example of a FSM lattice 30. In an example, theFSM lattice 30 comprises an array of blocks 32. As will be described,each block 32 may include a plurality of selectively couple-ablehardware elements (e.g., configurable elements and/or special purposeelements) that correspond to a plurality of states in a FSM. Similar toa state in a FSM, a hardware element can analyze an input stream andactivate a downstream hardware element, based on the input stream.

The configurable elements can be configured (e.g., programmed) toimplement many different functions. For instance, the configurableelements may include state machine elements (SMEs) 34, 36 (shown in FIG.5) that are hierarchically organized into rows 38 (shown in FIGS. 3 and4) and blocks 32 (shown in FIGS. 2 and 3). The SMEs may also beconsidered state transition elements (STEs). To route signals betweenthe hierarchically organized SMEs 34, 36, a hierarchy of configurableswitching elements can be used, including inter-block switching elements40 (shown in FIGS. 2 and 3), intra-block switching elements 42 (shown inFIGS. 3 and 4) and intra-row switching elements 44 (shown in FIG. 4).

As described below, the switching elements may include routingstructures and buffers. A SME 34, 36 can correspond to a state of a FSMimplemented by the FSM lattice 30. The SMEs 34, 36 can be coupledtogether by using the configurable switching elements as describedbelow. Accordingly, a FSM can be implemented on the FSM lattice 30 byconfiguring the SMEs 34, 36 to correspond to the functions of states andby selectively coupling together the SMEs 34, 36 to correspond to thetransitions between states in the FSM.

FIG. 2 illustrates an overall view of an example of a FSM lattice 30.The FSM lattice 30 includes a plurality of blocks 32 that can beselectively coupled together with configurable inter-block switchingelements 40. The inter-block switching elements 40 may includeconductors 46 (e.g., wires, traces, etc.) and buffers 48 and 50. In anexample, buffers 48 and 50 are included to control the connection andtiming of signals to/from the inter-block switching elements 40. Asdescribed further below, the buffers 48 may be provided to buffer databeing sent between blocks 32, while the buffers 50 may be provided tobuffer data being sent between inter-block switching elements 40.Additionally, the blocks 32 can be selectively coupled to an input block52 (e.g., a data input port) for receiving signals (e.g., data) andproviding the data to the blocks 32. The blocks 32 can also beselectively coupled to an output block 54 (e.g., an output port) forproviding signals from the blocks 32 to an external device (e.g.,another FSM lattice 30). The FSM lattice 30 can also include aprogramming interface 56 to configure (e.g., via an image, program) theFSM lattice 30. The image can configure (e.g., set) the state of theSMEs 34, 36. That is, the image can configure the SMEs 34, 36 to reactin a certain way to a given input at the input block 52. For example, aSME 34, 36 can be set to output a high signal when the character ‘a’ isreceived at the input block 52.

In an example, the input block 52, the output block 54, and/or theprogramming interface 56 can be implemented as registers such thatwriting to or reading from the registers provides data to or from therespective elements. Accordingly, bits from the image stored in theregisters corresponding to the programming interface 56 can be loaded onthe SMEs 34, 36. Although FIG. 2 illustrates a certain number ofconductors (e.g., wire, trace) between a block 32, input block 52,output block 54, and an inter-block switching element 40, it should beunderstood that in other examples, fewer or more conductors may be used.

FIG. 3 illustrates an example of a block 32. A block 32 can include aplurality of rows 38 that can be selectively coupled together withconfigurable intra-block switching elements 42. Additionally, a row 38can be selectively coupled to another row 38 within another block 32with the inter-block switching elements 40. A row 38 includes aplurality of SMEs 34, 36 organized into pairs of elements that arereferred to herein as groups of two (GOTs) 60. In an example, a block 32comprises sixteen (16) rows 38.

FIG. 4 illustrates an example of a row 38. A GOT 60 can be selectivelycoupled to other GOTs 60 and any other elements (e.g., a special purposeelement 58) within the row 38 by configurable intra-row switchingelements 44. A GOT 60 can also be coupled to other GOTs 60 in other rows38 with the intra-block switching element 42, or other GOTs 60 in otherblocks 32 with an inter-block switching element 40. In an example, a GOT60 has a first and second input 62, 64, and an output 66. The firstinput 62 is coupled to a first SME 34 of the GOT 60 and the second input64 is coupled to a second SME 36 of the GOT 60, as will be furtherillustrated with reference to FIG. 5.

In an example, the row 38 includes a first and second plurality of rowinterconnection conductors 68, 70. In an example, an input 62, 64 of aGOT 60 can be coupled to one or more row interconnection conductors 68,70, and an output 66 can be coupled to one or more row interconnectionconductor 68, 70. In an example, a first plurality of the rowinterconnection conductors 68 can be coupled to each SME 34, 36 of eachGOT 60 within the row 38. A second plurality of the row interconnectionconductors 70 can be coupled to only one SME 34, 36 of each GOT 60within the row 38, but cannot be coupled to the other SME 34, 36 of theGOT 60. In an example, a first half of the second plurality of rowinterconnection conductors 70 can couple to first half of the SMEs 34,36 within a row 38 (one SME 34 from each GOT 60) and a second half ofthe second plurality of row interconnection conductors 70 can couple toa second half of the SMEs 34, 36 within a row 38 (the other SME 34, 36from each GOT 60), as will be better illustrated with respect to FIG. 5.The limited connectivity between the second plurality of rowinterconnection conductors 70 and the SMEs 34, 36 is referred to hereinas “parity”. In an example, the row 38 can also include a specialpurpose element 58 such as a counter, a configurable Boolean logicelement, look-up table, RAM, a field configurable gate array (FPGA), anapplication specific integrated circuit (ASIC), a configurable processor(e.g., a microprocessor), or other element for performing a specialpurpose function.

In an example, the special purpose element 58 comprises a counter (alsoreferred to herein as counter 58). In an example, the counter 58comprises a 12-bit configurable down counter. The 12-bit configurablecounter 58 has a counting input, a reset input, and zero-count output.The counting input, when asserted, decrements the value of the counter58 by one. The reset input, when asserted, causes the counter 58 to loadan initial value from an associated register. For the 12-bit counter 58,up to a 12-bit number can be loaded in as the initial value. When thevalue of the counter 58 is decremented to zero (0), the zero-countoutput is asserted. The counter 58 also has at least two modes, pulseand hold. When the counter 58 is set to pulse mode, the zero-countoutput is asserted when the counter 58 reaches zero and the clockcycles. The zero-count output is asserted during the next clock cycle ofthe counter 58. Resulting in the counter 58 being offset in time fromthe clock cycle. At the next clock cycle, the zero-count output is nolonger asserted. When the counter 58 is set to hold mode the zero-countoutput is asserted during the clock cycle when the counter 58 decrementsto zero, and stays asserted until the counter 58 is reset by the resetinput being asserted.

In another example, the special purpose element 58 comprises Booleanlogic. For example, the Boolean logic may be used to perform logicalfunctions, such as AND, OR, NAND, NOR, Sum of Products (SoP),Negated-Output Sum of Products (NSoP), Negated-Output Product of Sume(NPoS), and Product of Sums (PoS) functions. This Boolean logic can beused to extract data from terminal state SMEs (corresponding to terminalnodes of a FSM, as discussed later herein) in FSM lattice 30. The dataextracted can be used to provide state data to other FSM lattices 30and/or to provide configuring data used to reconfigure FSM lattice 30,or to reconfigure another FSM lattice 30.

FIG. 5 illustrates an example of a GOT 60. The GOT 60 includes a firstSME 34 and a second SME 36 having inputs 62, 64 and having their outputs72, 74 coupled to an OR gate 76 and a 3-to-1 multiplexer 78. The 3-to-1multiplexer 78 can be set to couple the output 66 of the GOT 60 toeither the first SME 34, the second SME 36, or the OR gate 76. The ORgate 76 can be used to couple together both outputs 72, 74 to form thecommon output 66 of the GOT 60. In an example, the first and second SME34, 36 exhibit parity, as discussed above, where the input 62 of thefirst SME 34 can be coupled to some of the row interconnect conductors68 and the input 64 of the second SME 36 can be coupled to other rowinterconnect conductors 70 the common output 66 may be produced whichmay overcome parity problems. In an example, the two SMEs 34, 36 withina GOT 60 can be cascaded and/or looped back to themselves by settingeither or both of switching elements 79. The SMEs 34, 36 can be cascadedby coupling the output 72, 74 of the SMEs 34, 36 to the input 62, 64 ofthe other SME 34, 36. The SMEs 34, 36 can be looped back to themselvesby coupling the output 72, 74 to their own input 62, 64. Accordingly,the output 72 of the first SME 34 can be coupled to neither, one, orboth of the input 62 of the first SME 34 and the input 64 of the secondSME 36.

In an example, a state machine element 34, 36 comprises a plurality ofmemory cells 80, such as those often used in dynamic random accessmemory (DRAM), coupled in parallel to a detect line 82. One such memorycell 80 comprises a memory cell that can be set to a data state, such asone that corresponds to either a high or a low value (e.g., a 1 or 0).The output of the memory cell 80 is coupled to the detect line 82 andthe input to the memory cell 80 receives signals based on data on thedata stream line 84. In an example, an input at the input block 52 isdecoded to select one or more of the memory cells 80. The selectedmemory cell 80 provides its stored data state as an output onto thedetect line 82. For example, the data received at the input block 52 canbe provided to a decoder (not shown) and the decoder can select one ormore of the data stream lines 84. In an example, the decoder can convertan 8-bit ACSII character to the corresponding 1 of 256 data stream lines84.

A memory cell 80, therefore, outputs a high signal to the detect line 82when the memory cell 80 is set to a high value and the data on the datastream line 84 selects the memory cell 80. When the data on the datastream line 84 selects the memory cell 80 and the memory cell 80 is setto a low value, the memory cell 80 outputs a low signal to the detectline 82. The outputs from the memory cells 80 on the detect line 82 aresensed by a detection cell 86.

In an example, the signal on an input line 62, 64 sets the respectivedetection cell 86 to either an active or inactive state. When set to theinactive state, the detection cell 86 outputs a low signal on therespective output 72, 74 regardless of the signal on the respectivedetect line 82. When set to an active state, the detection cell 86outputs a high signal on the respective output line 72, 74 when a highsignal is detected from one of the memory cells 82 of the respective SME34, 36. When in the active state, the detection cell 86 outputs a lowsignal on the respective output line 72, 74 when the signals from all ofthe memory cells 82 of the respective SME 34, 36 are low.

In an example, an SME 34, 36 includes 256 memory cells 80 and eachmemory cell 80 is coupled to a different data stream line 84. Thus, anSME 34, 36 can be programmed to output a high signal when a selected oneor more of the data stream lines 84 have a high signal thereon. Forexample, the SME 34 can have a first memory cell 80 (e.g., bit 0) sethigh and all other memory cells 80 (e.g., bits 1-255) set low. When therespective detection cell 86 is in the active state, the SME 34 outputsa high signal on the output 72 when the data stream line 84corresponding to bit 0 has a high signal thereon. In other examples, theSME 34 can be set to output a high signal when one of multiple datastream lines 84 have a high signal thereon by setting the appropriatememory cells 80 to a high value.

In an example, a memory cell 80 can be set to a high or low value byreading bits from an associated register. Accordingly, the SMEs 34 canbe configured by storing an image created by the compiler 20 into theregisters and loading the bits in the registers into associated memorycells 80. In an example, the image created by the compiler 20 includes abinary image of high and low (e.g., 1 and 0) bits. The image canconfigure the FSM lattice 30 to implement a FSM by cascading the SMEs34, 36. For example, a first SME 34 can be set to an active state bysetting the detection cell 86 to the active state. The first SME 34 canbe set to output a high signal when the data stream line 84corresponding to bit 0 has a high signal thereon. The second SME 36 canbe initially set to an inactive state, but can be set to, when active,output a high signal when the data stream line 84 corresponding to bit 1has a high signal thereon. The first SME 34 and the second SME 36 can becascaded by setting the output 72 of the first SME 34 to couple to theinput 64 of the second SME 36. Thus, when a high signal is sensed on thedata stream line 84 corresponding to bit 0, the first SME 34 outputs ahigh signal on the output 72 and sets the detection cell 86 of thesecond SME 36 to an active state. When a high signal is sensed on thedata stream line 84 corresponding to bit 1, the second SME 36 outputs ahigh signal on the output 74 to activate another SME 36 or for outputfrom the FSM lattice 30.

In an example, a single FSM lattice 30 is implemented on a singlephysical device, however, in other examples two or more FSM lattices 30can be implemented on a single physical device (e.g., physical chip). Inan example, each FSM lattice 30 can include a distinct data input block52, a distinct output block 54, a distinct programming interface 56, anda distinct set of configurable elements. Moreover, each set ofconfigurable elements can react (e.g., output a high or low signal) todata at their corresponding data input block 52. For example, a firstset of configurable elements corresponding to a first FSM lattice 30 canreact to the data at a first data input block 52 corresponding to thefirst FSM lattice 30. A second set of configurable elementscorresponding to a second FSM lattice 30 can react to a second datainput block 52 corresponding to the second FSM lattice 30. Accordingly,each FSM lattice 30 includes a set of configurable elements, whereindifferent sets of configurable elements can react to different inputdata. Similarly, each FSM lattice 30, and each corresponding set ofconfigurable elements can provide a distinct output. In some examples,an output block 54 from a first FSM lattice 30 can be coupled to aninput block 52 of a second FSM lattice 30, such that input data for thesecond FSM lattice 30 can include the output data from the first FSMlattice 30 in a hierarchical arrangement of a series of FSM lattices 30.

In an example, an image for loading onto the FSM lattice 30 comprises aplurality of bits of data for configuring the configurable elements, theconfigurable switching elements, and the special purpose elements withinthe FSM lattice 30. In an example, the image can be loaded onto the FSMlattice 30 to configure the FSM lattice 30 to provide a desired outputbased on certain inputs. The output block 54 can provide outputs fromthe FSM lattice 30 based on the reaction of the configurable elements todata at the data input block 52. An output from the output block 54 caninclude a single bit indicating a match of a given pattern, a wordcomprising a plurality of bits indicating matches and non-matches to aplurality of patterns, and a state vector corresponding to the state ofall or certain configurable elements at a given moment. As described, anumber of FSM lattices 30 may be included in a state machine engine,such as state machine engine 14, to perform data analysis, such aspattern-recognition (e.g., speech recognition, image recognition, etc.)signal processing, imaging, computer vision, cryptography, and others.

FIG. 6 illustrates an example model of a finite state machine (FSM) thatcan be implemented by the FSM lattice 30. The FSM lattice 30 can beconfigured (e.g., programmed) as a physical implementation of a FSM. AFSM can be represented as a diagram 90, (e.g., directed graph,undirected graph, pseudograph), which contains one or more root nodes92. In addition to the root nodes 92, the FSM can be made up of severalstandard nodes 94 and terminal nodes 96 that are connected to the rootnodes 92 and other standard nodes 94 through one or more edges 98. Anode 92, 94, 96 corresponds to a state in the FSM. The edges 98correspond to the transitions between the states.

Each of the nodes 92, 94, 96 can be in either an active or an inactivestate. When in the inactive state, a node 92, 94, 96 does not react(e.g., respond) to input data. When in an active state, a node 92, 94,96 can react to input data. An upstream node 92, 94 can react to theinput data by activating a node 94, 96 that is downstream from the nodewhen the input data matches criteria specified by an edge 98 between theupstream node 92, 94 and the downstream node 94, 96. For example, afirst node 94 that specifies the character ‘b’ will activate a secondnode 94 connected to the first node 94 by an edge 98 when the first node94 is active and the character ‘b’ is received as input data. As usedherein, “upstream” refers to a relationship between one or more nodes,where a first node that is upstream of one or more other nodes (orupstream of itself in the case of a loop or feedback configuration)refers to the situation in which the first node can activate the one ormore other nodes (or can activate itself in the case of a loop).Similarly, “downstream” refers to a relationship where a first node thatis downstream of one or more other nodes (or downstream of itself in thecase of a loop) can be activated by the one or more other nodes (or canbe activated by itself in the case of a loop). Accordingly, the terms“upstream” and “downstream” are used herein to refer to relationshipsbetween one or more nodes, but these terms do not preclude the use ofloops or other non-linear paths among the nodes.

In the diagram 90, the root node 92 can be initially activated and canactivate downstream nodes 94 when the input data matches an edge 98 fromthe root node 92. Nodes 94 can activate nodes 96 when the input datamatches an edge 98 from the node 94. Nodes 94, 96 throughout the diagram90 can be activated in this manner as the input data is received. Aterminal node 96 corresponds to a match of a sequence of interest by theinput data. Accordingly, activation of a terminal node 96 indicates thata sequence of interest has been received as the input data. In thecontext of the FSM lattice 30 implementing a pattern recognitionfunction, arriving at a terminal node 96 can indicate that a specificpattern of interest has been detected in the input data.

In an example, each root node 92, standard node 94, and terminal node 96can correspond to a configurable element in the FSM lattice 30. Eachedge 98 can correspond to connections between the configurable elements.Thus, a standard node 94 that transitions to (e.g., has an edge 98connecting to) another standard node 94 or a terminal node 96corresponds to a configurable element that transitions to (e.g.,provides an output to) another configurable element. In some examples,the root node 92 does not have a corresponding configurable element.

As will be appreciated, although the node 92 is described as a root nodeand nodes 96 are described as terminal nodes, there may not necessarilybe a particular “start” or root node and there may not necessarily be aparticular “end” or output node. In other words, any node may be astarting point and any node may provide output.

When the FSM lattice 30 is programmed, each of the configurable elementscan also be in either an active or inactive state. A given configurableelement, when inactive, does not react to the input data at acorresponding data input block 52. An active configurable element canreact to the input data at the data input block 52, and can activate adownstream configurable element when the input data matches the settingof the configurable element. When a configurable element corresponds toa terminal node 96, the configurable element can be coupled to theoutput block 54 to provide an indication of a match to an externaldevice.

An image loaded onto the FSM lattice 30 via the programming interface 56can configure the configurable elements and special purpose elements, aswell as the connections between the configurable elements and specialpurpose elements, such that a desired FSM is implemented through thesequential activation of nodes based on reactions to the data at thedata input block 52. In an example, a configurable element remainsactive for a single data cycle (e.g., a single character, a set ofcharacters, a single clock cycle) and then becomes inactive unlessre-activated by an upstream configurable element.

A terminal node 96 can be considered to store a compressed history ofpast events. For example, the one or more patterns of input datarequired to reach a terminal node 96 can be represented by theactivation of that terminal node 96. In an example, the output providedby a terminal node 96 is binary, that is, the output indicates whetherthe pattern of interest has been matched or not. The ratio of terminalnodes 96 to standard nodes 94 in a diagram 90 may be quite small. Inother words, although there may be a high complexity in the FSM, theoutput of the FSM may be small by comparison.

In an example, the output of the FSM lattice 30 can comprise a statevector. The state vector comprises the state (e.g., activated or notactivated) of configurable elements of the FSM lattice 30. In anotherexample, the state vector can include the state of all or a subset ofthe configurable elements whether or not the configurable elementscorresponds to a terminal node 96. In an example, the state vectorincludes the states for the configurable elements corresponding toterminal nodes 96. Thus, the output can include a collection of theindications provided by all terminal nodes 96 of a diagram 90. The statevector can be represented as a word, where the binary indicationprovided by each terminal node 96 comprises one bit of the word. Thisencoding of the terminal nodes 96 can provide an effective indication ofthe detection state (e.g., whether and what sequences of interest havebeen detected) for the FSM lattice 30.

As mentioned above, the FSM lattice 30 can be programmed to implement apattern recognition function. For example, the FSM lattice 30 can beconfigured to recognize one or more data sequences (e.g., signatures,patterns) in the input data. When a data sequence of interest isrecognized by the FSM lattice 30, an indication of that recognition canbe provided at the output block 54. In an example, the patternrecognition can recognize a string of symbols (e.g., ASCII characters)to, for example, identify malware or other data in network data.

FIG. 7 illustrates an example of hierarchical structure 100, wherein twolevels of FSM lattices 30 are coupled in series and used to analyzedata. Specifically, in the illustrated embodiment, the hierarchicalstructure 100 includes a first FSM lattice 30A and a second FSM lattice30B arranged in series. Each FSM lattice 30 includes a respective datainput block 52 to receive data input, a programming interface block 56to receive configuring signals and an output block 54.

The first FSM lattice 30A is configured to receive input data, forexample, raw data at a data input block. The first FSM lattice 30Areacts to the input data as described above and provides an output at anoutput block. The output from the first FSM lattice 30A is sent to adata input block of the second FSM lattice 30B. The second FSM lattice30B can then react based on the output provided by the first FSM lattice30A and provide a corresponding output signal 102 of the hierarchicalstructure 100. This hierarchical coupling of two FSM lattices 30A and30B in series provides a means to provide data regarding past events ina compressed word from a first FSM lattice 30A to a second FSM lattice30B. The data provided can effectively be a summary of complex events(e.g., sequences of interest) that were recorded by the first FSMlattice 30A.

The two-level hierarchy 100 of FSM lattices 30A, 30B shown in FIG. 7allows two independent programs to operate based on the same datastream. The two-stage hierarchy can be similar to visual recognition ina biological brain which is modeled as different regions. Under thismodel, the regions are effectively different pattern recognitionengines, each performing a similar computational function (patternmatching) but using different programs (signatures). By connectingmultiple FSM lattices 30A, 30B together, increased knowledge about thedata stream input may be obtained.

The first level of the hierarchy (implemented by the first FSM lattice30A) can, for example, perform processing directly on a raw data stream.That is, a raw data stream can be received at an input block 52 of thefirst FSM lattice 30A and the configurable elements of the first FSMlattice 30A can react to the raw data stream. The second level(implemented by the second FSM lattice 30B) of the hierarchy can processthe output from the first level. That is, the second FSM lattice 30Breceives the output from an output block 54 of the first FSM lattice 30Aat an input block 52 of the second FSM lattice 30B and the configurableelements of the second FSM lattice 30B can react to the output of thefirst FSM lattice 30A. Accordingly, in this example, the second FSMlattice 30B does not receive the raw data stream as an input, but ratherreceives the indications of patterns of interest that are matched by theraw data stream as determined by the first FSM lattice 30A. The secondFSM lattice 30B can implement a FSM that recognizes patterns in theoutput data stream from the first FSM lattice 30A. It should beappreciated that the second FSM lattice 30B may receive inputs frommultiple other FSM lattices in addition to receiving output from the FSMlattice 30A. Likewise, the second FSM lattice 30B may receive inputsfrom other devices. The second FSM lattice 30B may combine thesemultiple inputs to produce outputs.

FIG. 8 illustrates an example of a method 110 for a compiler to convertsource code into an image configured to configure a FSM lattice, such aslattice 30, to implement a FSM. Method 110 includes parsing the sourcecode into a syntax tree (block 112), converting the syntax tree into anautomaton (block 114), optimizing the automaton (block 116), convertingthe automaton into a netlist (block 118), placing the netlist onhardware (block 120), routing the netlist (block 122), and publishingthe resulting image (block 124).

In an example, the compiler 20 includes an application programminginterface (API) that allows software developers to create images forimplementing FSMs on the FSM lattice 30. The compiler 20 providesmethods to convert an input set of regular expressions in the sourcecode into an image that is configured to configure the FSM lattice 30.The compiler 20 can be implemented by instructions for a computer havinga von Neumann architecture. These instructions can cause a processor 12on the computer to implement the functions of the compiler 20. Forexample, the instructions, when executed by the processor 12, can causethe processor 12 to perform actions as described in blocks 112, 114,116, 118, 120, 122, and 124 on source code that is accessible to theprocessor 12.

In an example, the source code describes search strings for identifyingpatterns of symbols within a group of symbols. To describe the searchstrings, the source code can include a plurality of regular expressions(regexs). A regex can be a string for describing a symbol searchpattern. Regexes are widely used in various computer domains, such asprogramming languages, text editors, network security, and others. In anexample, the regular expressions supported by the compiler includecriteria for the analysis of unstructured data. Unstructured data caninclude data that is free form and has no indexing applied to wordswithin the data. Words can include any combination of bytes, printableand non-printable, within the data. In an example, the compiler cansupport multiple different source code languages for implementingregexes including Perl, (e.g., Perl compatible regular expressions(PCRE)), PHP, Java, and .NET languages.

At block 112 the compiler 20 can parse the source code to form anarrangement of relationally connected operators, where different typesof operators correspond to different functions implemented by the sourcecode (e.g., different functions implemented by regexes in the sourcecode). Parsing source code can create a generic representation of thesource code. In an example, the generic representation comprises anencoded representation of the regexs in the source code in the form of atree graph known as a syntax tree. The examples described herein referto the arrangement as a syntax tree (also known as an “abstract syntaxtree”) in other examples, however, a concrete syntax tree or otherarrangement can be used.

Since, as mentioned above, the compiler 20 can support multiplelanguages of source code, parsing converts the source code, regardlessof the language, into a non-language specific representation, e.g., asyntax tree. Thus, further processing (blocks 114, 116, 118, 120) by thecompiler 20 can work from a common input structure regardless of thelanguage of the source code.

As noted above, the syntax tree includes a plurality of operators thatare relationally connected. A syntax tree can include multiple differenttypes of operators. That is, different operators can correspond todifferent functions implemented by the regexes in the source code.

At block 114, the syntax tree is converted into an automaton. Anautomaton comprises a software model of a FSM and can accordingly beclassified as deterministic or non-deterministic. A deterministicautomaton has a single path of execution at a given time, while anon-deterministic automaton has multiple concurrent paths of execution.The automaton comprises a plurality of states. In order to convert thesyntax tree into an automaton, the operators and relationships betweenthe operators in the syntax tree are converted into states withtransitions between the states. In an example, the automaton can beconverted based partly on the hardware of the FSM lattice 30.

In an example, input symbols for the automaton include the symbols ofthe alphabet, the numerals 0-9, and other printable characters. In anexample, the input symbols are represented by the byte values 0 through255 inclusive. In an example, an automaton can be represented as adirected graph where the nodes of the graph correspond to the set ofstates. In an example, a transition from state p to state q on an inputsymbol α, i.e. δ(p,α), is shown by a directed connection from node p tonode q. In an example, a reversal of an automaton produces a newautomaton where each transition p→q on some symbol α is reversed q→p onthe same symbol. In a reversal, start state becomes a final state andthe final states become start states. In an example, the languagerecognized (e.g., matched) by an automaton is the set of all possiblecharacter strings which when input sequentially into the automaton willreach a final state. Each string in the language recognized by theautomaton traces a path from the start state to one or more finalstates.

At block 116, after the automaton is constructed, the automaton isoptimized to, among other things, reduce its complexity and size. Theautomaton can be optimized by combining redundant states.

At block 118, the optimized automaton is converted into a netlist.Converting the automaton into a netlist maps each state of the automatonto a hardware element (e.g., SMEs 34, 36, other elements) on the FSMlattice 30, and determines the connections between the hardwareelements.

At block 120, the netlist is placed to select a specific hardwareelement of the target device (e.g., SMEs 34, 36, special purposeelements 58) corresponding to each node of the netlist. In an example,placing selects each specific hardware element based on general inputand output constraints for of the FSM lattice 30.

At block 122, the placed netlist is routed to determine the settings forthe configurable switching elements (e.g., inter-block switchingelements 40, intra-block switching elements 42, and intra-row switchingelements 44) in order to couple the selected hardware elements togetherto achieve the connections describe by the netlist. In an example, thesettings for the configurable switching elements are determined bydetermining specific conductors of the FSM lattice 30 that will be usedto connect the selected hardware elements, and the settings for theconfigurable switching elements. Routing can take into account morespecific limitations of the connections between the hardware elementsthat placement at block 120. Accordingly, routing may adjust thelocation of some of the hardware elements as determined by the globalplacement in order to make appropriate connections given the actuallimitations of the conductors on the FSM lattice 30.

Once the netlist is placed and routed, the placed and routed netlist canbe converted into a plurality of bits for configuring of a FSM lattice30. The plurality of bits are referred to herein as an image.

At block 124, an image is published by the compiler 20. The imagecomprises a plurality of bits for configuring specific hardware elementsof the FSM lattice 30. In embodiments where the image comprises aplurality of bits (e.g., 0 and 1), the image can be referred to as abinary image. The bits can be loaded onto the FSM lattice 30 toconfigure the state of SMEs 34, 36, the special purpose elements 58, andthe configurable switching elements such that the programmed FSM lattice30 implements a FSM having the functionality described by the sourcecode. Placement (block 120) and routing (block 122) can map specifichardware elements at specific locations in the FSM lattice 30 tospecific states in the automaton. Accordingly, the bits in the image canconfigure the specific hardware elements to implement the desiredfunction(s). In an example, the image can be published by saving themachine code to a computer readable medium. In another example, theimage can be published by displaying the image on a display device. Instill another example, the image can be published by sending the imageto another device, such as a configuring device for loading the imageonto the FSM lattice 30. In yet another example, the image can bepublished by loading the image onto a FSM lattice (e.g., the FSM lattice30).

In an example, an image can be loaded onto the FSM lattice 30 by eitherdirectly loading the bit values from the image to the SMEs 34, 36 andother hardware elements or by loading the image into one or moreregisters and then writing the bit values from the registers to the SMEs34, 36 and other hardware elements. In an example, the hardware elements(e.g., SMEs 34, 36, special purpose elements 58, configurable switchingelements 40, 42, 44) of the FSM lattice 30 are memory mapped such that aconfiguring device and/or computer can load the image onto the FSMlattice 30 by writing the image to one or more memory addresses.

Method examples described herein can be machine or computer-implementedat least in part. Some examples can include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods can include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code can include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, the code may be tangibly stored on one ormore volatile or non-volatile computer-readable media during executionor at other times. These computer-readable media may include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

Referring now to FIG. 9, an embodiment of the state machine engine 14(e.g., a single device on a single chip) is illustrated. As previouslydescribed, the state machine engine 14 is configured to receive datafrom a source, such as the memory 16 over a data bus. In the illustratedembodiment, data may be sent to the state machine engine 14 through abus interface, such as a double data rate three (DDR3) bus interface130. The DDR3 bus interface 130 may be capable of exchanging (e.g.,providing and receiving) data at a rate greater than or equal to 1GByte/sec. Such a data exchange rate may be greater than a rate thatdata is analyzed by the state machine engine 14. As will be appreciated,depending on the source of the data to be analyzed, the bus interface130 may be any suitable bus interface for exchanging data to and from adata source to the state machine engine 14, such as a NAND Flashinterface, peripheral component interconnect (PCI) interface, gigabitmedia independent interface (GMMI), etc. As previously described, thestate machine engine 14 includes one or more FSM lattices 30 configuredto analyze data. Each FSM lattice 30 may be divided into twohalf-lattices. In the illustrated embodiment, each half lattice mayinclude 24K SMEs (e.g., SMEs 34, 36), such that the lattice 30 includes48K SMEs. The lattice 30 may comprise any desirable number of SMEs,arranged as previously described with regard to FIGS. 2-5. Further,while only one FSM lattice 30 is illustrated, the state machine engine14 may include multiple FSM lattices 30, as previously described.

Data to be analyzed may be received at the bus interface 130 andprovided to the FSM lattice 30 through a number of buffers and bufferinterfaces. In the illustrated embodiment, the data path includes databuffers 132, an instruction buffer 133, process buffers 134, and aninter-rank (IR) bus and process buffer interface 136. The data buffers132 are configured to receive and temporarily store data to be analyzed.In one embodiment, there are two data buffers 132 (data buffer A anddata buffer B). Data may be stored in one of the two data buffers 132,while data is being emptied from the other data buffer 132, for analysisby the FSM lattice 30. The bus interface 130 may be configured toprovide data to be analyzed to the data buffers 132 until the databuffers 132 are full. After the data buffers 132 are full, the businterface 130 may be configured to be free to be used for other purpose(e.g., to provide other data from a data stream until the data buffers132 are available to receive additional data to be analyzed). In theillustrated embodiment, the data buffers 132 may be 32 KBytes each. Theinstruction buffer 133 is configured to receive instructions from theprocessor 12 via the bus interface 130, such as instructions thatcorrespond to the data to be analyzed and instructions that correspondto configuring the state machine engine 14. The IR bus and processbuffer interface 136 may facilitate providing data to the process buffer134. The IR bus and process buffer interface 136 can be used to ensurethat data is processed by the FSM lattice 30 in order. The IR bus andprocess buffer interface 136 may coordinate the exchange of data, timingdata, packing instructions, etc. such that data is received and analyzedcorrectly. Generally, the IR bus and process buffer interface 136 allowsthe analyzing of multiple data sets in parallel through a logical rankof FSM lattices 30. For example, multiple physical devices (e.g., statemachine engines 14, chips, separate devices) may be arranged in a rankand may provide data to each other via the IR bus and process bufferinterface 136. For purposes of this application the term “rank” refersto a set of state machine engines 14 connected to the same chip select.In the illustrated embodiment, the IR bus and process buffer interface136 may include a 32 bit data bus. In other embodiments, the IR bus andprocess buffer interface 136 may include any suitable data bus, such asa 128 bit data bus.

In the illustrated embodiment, the state machine engine 14 also includesa de-compressor 138 and a compressor 140 to aid in providing statevector data through the state machine engine 14. The compressor 140 andde-compressor 138 work in conjunction such that the state vector datacan be compressed to minimize the data providing times. By compressingthe state vector data, the bus utilization time may be minimized. Thecompressor 140 and de-compressor 138 can also be configured to handlestate vector data of varying burst lengths. By padding compressed statevector data and including an indicator as to when each compressed regionends, the compressor 140 may improve the overall processing speedthrough the state machine engine 14. The compressor 140 may be used tocompress match results data after analysis by the FSM lattice 30. Thecompressor 140 and de-compressor 138 may also be used to compress anddecompress configuration data. In one embodiment, the compressor 140 andde-compressor 138 may be disabled (e.g., turned off) such that dataflowing to and/or from the compressor 140 and de-compressor 138 is notmodified.

As previously described, an output of the FSM lattice 30 can comprise astate vector. The state vector comprises the state (e.g., activated ornot activated) of the SMEs 34, 36 of the FSM lattice 30 and the dynamic(e.g., current) count of the counter 58. The state machine engine 14includes a state vector system 141 having a state vector cache memory142, a state vector memory buffer 144, a state vector intermediate inputbuffer 146, and a state vector intermediate output buffer 148. The statevector system 141 may be used to store multiple state vectors of the FSMlattice 30 and to provide a state vector to the FSM lattice 30 torestore the FSM lattice 30 to a state corresponding to the providedstate vector. For example, each state vector may be temporarily storedin the state vector cache memory 142. That is, the state of each SME 34,36 may be stored, such that the state may be restored and used infurther analysis at a later time, while freeing the SMEs 34, 36 forfurther analysis of a new data set (e.g., search term). Like a typicalcache, the state vector cache memory 142 allows storage of state vectorsfor quick retrieval and use, here by the FSM lattice 30, for instance.In the illustrated embodiment, the state vector cache memory 142 maystore up to 512 state vectors.

As will be appreciated, the state vector data may be exchanged betweendifferent state machine engines 14 (e.g., chips) in a rank. The statevector data may be exchanged between the different state machine engines14 for various purposes such as: to synchronize the state of the SMEs34, 36 of the FSM lattices 30 of the state machine engines 14, toperform the same functions across multiple state machine engines 14, toreproduce results across multiple state machine engines 14, to cascaderesults across multiple state machine engines 14, to store a history ofstates of the SMEs 34, 36 used to analyze data that is cascaded throughmultiple state machine engines 14, and so forth. Furthermore, it shouldbe noted that within a state machine engine 14, the state vector datamay be used to quickly configure the SMEs 34, 36 of the FSM lattice 30.For example, the state vector data may be used to restore the state ofthe SMEs 34, 36 to an initialized state (e.g., to search for a newsearch term), to restore the state of the SMEs 34, 36 to prior state(e.g., to search for a previously searched search term), and to changethe state of the SMEs 34, 36 to be configured for a cascadingconfiguration (e.g., to search for a search term in a cascading search).In certain embodiments, the state vector data may be provided to the businterface 130 so that the state vector data may be provided to theprocessor 12 (e.g., for analysis of the state vector data, reconfiguringthe state vector data to apply modifications, reconfiguring the statevector data to improve efficiency of the SMEs 34, 36, and so forth).

For example, in certain embodiments, the state machine engine 14 mayprovide cached state vector data (e.g., data stored by the state vectorsystem 141) from the FSM lattice 30 to an external device. The externaldevice may receive the state vector data, modify the state vector data,and provide the modified state vector data to the state machine engine14 for configuring the FSM lattice 30. Accordingly, the external devicemay modify the state vector data so that the state machine engine 14 mayskip states (e.g., jump around) as desired.

The state vector cache memory 142 may receive state vector data from anysuitable device. For example, the state vector cache memory 142 mayreceive a state vector from the FSM lattice 30, another FSM lattice 30(e.g., via the IR bus and process buffer interface 136), thede-compressor 138, and so forth. In the illustrated embodiment, thestate vector cache memory 142 may receive state vectors from otherdevices via the state vector memory buffer 144. Furthermore, the statevector cache memory 142 may provide state vector data to any suitabledevice. For example, the state vector cache memory 142 may provide statevector data to the state vector memory buffer 144, the state vectorintermediate input buffer 146, and the state vector intermediate outputbuffer 148.

Additional buffers, such as the state vector memory buffer 144, statevector intermediate input buffer 146, and state vector intermediateoutput buffer 148, may be utilized in conjunction with the state vectorcache memory 142 to accommodate rapid retrieval and storage of statevectors, while processing separate data sets with interleaved packetsthrough the state machine engine 14. In the illustrated embodiment, eachof the state vector memory buffer 144, the state vector intermediateinput buffer 146, and the state vector intermediate output buffer 148may be configured to temporarily store one state vector. The statevector memory buffer 144 may be used to receive state vector data fromany suitable device and to provide state vector data to any suitabledevice. For example, the state vector memory buffer 144 may be used toreceive a state vector from the FSM lattice 30, another FSM lattice 30(e.g., via the IR bus and process buffer interface 136), thede-compressor 138, and the state vector cache memory 142. As anotherexample, the state vector memory buffer 144 may be used to provide statevector data to the IR bus and process buffer interface 136 (e.g., forother FSM lattices 30), the compressor 140, and the state vector cachememory 142.

Likewise, the state vector intermediate input buffer 146 may be used toreceive state vector data from any suitable device and to provide statevector data to any suitable device. For example, the state vectorintermediate input buffer 146 may be used to receive a state vector froman FSM lattice 30 (e.g., via the IR bus and process buffer interface136), the de-compressor 138, and the state vector cache memory 142. Asanother example, the state vector intermediate input buffer 146 may beused to provide a state vector to the FSM lattice 30. Furthermore, thestate vector intermediate output buffer 148 may be used to receive astate vector from any suitable device and to provide a state vector toany suitable device. For example, the state vector intermediate outputbuffer 148 may be used to receive a state vector from the FSM lattice 30and the state vector cache memory 142. As another example, the statevector intermediate output buffer 148 may be used to provide a statevector to an FSM lattice 30 (e.g., via the IR bus and process bufferinterface 136) and the compressor 140.

Once a result of interest is produced by the FSM lattice 30, matchresults may be stored in a match results memory 150. That is, a “matchvector” indicating a match (e.g., detection of a pattern of interest)may be stored in the match results memory 150. The match result can thenbe sent to a match buffer 152 for transmission over the bus interface130 to the processor 12, for example. As previously described, the matchresults may be compressed.

Additional registers and buffers may be provided in the state machineengine 14, as well. For instance, the state machine engine 14 mayinclude control and status registers 154. In addition, a program buffersystem (e.g., repair map and program buffers 156) may be provided forprogramming the FSM lattice 30 initially. For example, initial (e.g.,starting) state vector data may be provided from the program buffersystem to the FSM lattice 30 (e.g., via the de-compressor 138). Thede-compressor 138 may be used to decompress configuration data (e.g.,state vector data, routing switch data, SME 34, 36 states, Booleanfunction data, counter data, match MUX data) provided to program the FSMlattice 30.

Similarly, a repair map buffer system (e.g., save and repair map buffers158) may also be provided for storage of data (e.g., save and repairmaps) for setup and usage. The data stored by the repair map buffersystem may include data that corresponds to repaired hardware elements,such as data identifying which SMEs 34, 36 were repaired. The repair mapbuffer system may receive data via any suitable manner. For example,data may be provided from a “fuse map” memory, which provides themapping of repairs done on a device during final manufacturing testing,to the repair map buffers 158. As another example, the repair map buffersystem may include data used to modify (e.g., customize) a standardprogramming file so that the standard programming file may operate in aFSM lattice 30 with a repaired architecture (e.g., bad SMEs 34, 36 in aFSM lattice 30 may be bypassed so they are not used). The compressor 140may be used to compress data provided to the repair map buffers 158 fromthe fuse map memory. As illustrated, the bus interface 130 may be usedto provide data to the program buffers 156 and to provide data from therepair map buffers 158. As will be appreciated, the data provided to theprogram buffers 156 and/or provided from the repair map buffers 158 maybe compressed. In some embodiments, data is provided to the businterface 130 and/or received from the bus interface 130 via a deviceexternal to the state machine engine 14 (e.g., the processor 12, thememory 16, the compiler 20, and so forth). The device external to thestate machine engine 14 may be configured to receive data provided fromthe repair map buffers 158, to store the data, to analyze the data, tomodify the data, and/or to provide new or modified data to the programbuffers 156.

The state machine engine 14 includes a lattice programming system 159used to program the FSM lattice 30. As illustrated, the latticeprogramming system 159 may receive data (e.g., configurationinstructions) from the instruction queue 133. Furthermore, the latticeprogramming system 159 may receive data (e.g., configuration data) fromthe program buffers 156. The lattice programming system 159 may use theconfiguration instructions and the configuration data to configure theFSM lattice 30 (e.g., to configure routing switches, SMEs 34, 36,Boolean cells, counters, match MUX). The lattice programming system 159may use the de-compressor 138 to compress data and the compressor 140 tocompress data (e.g., for data exchanged with the program buffers 156 andthe repair map buffers 158.

Referring now to FIG. 10, a flow chart showing a method 160 forprogramming the FSM lattice 30 in the state machine engine 14 isillustrated. The method 160 may include providing configuration data tothe program buffers 156 (block 162), providing configurationinstructions to the instruction buffer 133 (block 164), initiating aconfiguration update (block 166), reading instructions from theinstruction buffer 133 (block 168), determining whether the instructionsare configuration instructions (block 170), reading configuration datafrom the program buffers 156 and de-compressing the configuration dataif compressed (block 172), and providing the configuration data to anFSM lattice 30 (block 174).

In one embodiment, at block 162, the processor 12 may provideconfiguration data to the program buffer system (e.g., restore map andprogram buffers 156) via the bus interface 130. Further, the processor12 may provide configuration instructions to the instruction buffer 133via the bus interface 130 (block 164). At block 166, a configurationupdate may be initiated (e.g., via the processor 12, the latticeprogramming system 159, or the state machine engine 14). Then, at block168, the lattice programming system 159 may read the instructions storedin the instruction buffer 133. Next, at block 170, the latticeprogramming system 159 may determine whether the instructions read fromthe instruction buffer 133 are configuration instructions. If theinstructions are not configuration instructions, the lattice programmingsystem 159 reads additions instructions stored in the instruction buffer133 at block 168. However, if the instructions are configurationinstructions, the lattice programming system 159 reads configurationdata stored in the program buffer system (block 172). In someembodiments, the configuration data may be compressed. Accordingly, alsoat block 172, the lattice programming system 159 may decompress theconfiguration data (e.g., via the de-compressor 138). The latticeprogramming system 159 then provides the configuration data to the FSMlattice 30 (block 174).

While the invention may be susceptible to various modifications andalternative forms, specific embodiments have been shown by way ofexample in the drawings and have been described in detail herein.However, it should be understood that the invention is not intended tobe limited to the particular forms disclosed. Rather, the invention isto cover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention as defined by the followingappended claims.

What is claimed is:
 1. A state machine engine comprising: a programbuffer configured to receive configuration data via a bus interface forconfiguring a state machine lattice; and a repair map buffer configuredto provide repair map data to an external device via the bus interface;wherein the state machine lattice comprises a plurality of programmableelements and each programmable element comprises a plurality of memorycells configured to analyze data and to output a result of the analysis.2. The state machine engine of claim 1, wherein the program buffer isconfigured to receive compressed configuration data.
 3. The statemachine engine of claim 1, wherein the program buffer is configured toreceive non-compressed configuration data.
 4. The state machine engineof claim 1, wherein the repair map buffer is configured to providecompressed repair map data.
 5. The state machine engine of claim 1,wherein the repair map buffer is configured to provide non-compressedrepair map data.
 6. The state machine engine of claim 1, wherein theexternal device is configured to receive repair map data provided fromthe repair map buffer, to analyze the repair map data, to construct amodified version of configuration data, and to provide the modifiedversion of configuration data to the program buffer.
 7. The statemachine engine of claim 1, wherein the program buffer is configured toprovide configuration data to a lattice programming system to configurethe state machine lattice.
 8. A state machine engine comprising aprogram buffer configured to receive configuration data via a businterface for configuring a state machine lattice, wherein the statemachine lattice comprises a plurality of programmable elements and eachprogrammable element comprises a plurality of memory cells configured toanalyze at least a portion of a data stream and to output a result ofthe analysis.
 9. The state machine engine of claim 8, wherein theprogram buffer is configured to provide configuration data to a latticeprogramming system.
 10. The state machine engine of claim 9, wherein thelattice programming system is configured to provide configuration datato the state machine lattice to configure the state machine lattice. 11.The state machine engine of claim 8, wherein the program buffer isconfigured to provide compressed configuration data to a latticeprogramming system.
 12. The state machine engine of claim 11, whereinthe lattice programming system is configured to decompress theconfiguration data after receiving the compressed configuration data.13. A state machine engine comprising a repair map buffer configured toprovide repair map data from a state machine lattice to an externaldevice via a bus interface, wherein the state machine lattice comprisesa plurality of programmable elements and each programmable elementcomprises a plurality of memory cells configured to analyze at least aportion of a data stream and to output a result of the analysis.
 14. Thestate machine engine of claim 13, wherein the repair buffer isconfigured to receive configuration data from a lattice programmingsystem.
 15. The state machine engine of claim 13, wherein the repairbuffer is configured to receive compressed repair map data from alattice programming system.
 16. The state machine engine of claim 15,wherein the lattice programming system is configured to compress therepair map data before providing the repair map data to the repairbuffer.
 17. The state machine engine of claim 13, wherein the externaldevice is configured to store the repair map data.
 18. The state machineengine of claim 13, wherein the external device is configured to analyzethe repair map data and to provide modified configuration data to thestate machine engine.
 19. The state machine engine of claim 13, whereinthe repair map data comprises data that corresponds to repaired portionsof the state machine lattice.
 20. The state machine engine of claim 13,wherein the repair map data comprises an indication identifying whichportions of the state machine lattice were repaired and an indicationidentifying how the state machine lattice was repaired.
 21. A method forconfiguring a state machine lattice of a state machine engine,comprising: receiving configuration data at a program buffer of thestate machine engine; providing the configuration data from the programbuffer to a lattice programming system; and configuring the statemachine lattice using the lattice programming system, wherein the statemachine lattice comprises a plurality of programmable elements and eachprogrammable element comprises a plurality of memory cells configured toanalyze at least a portion of a data stream and to output a result ofthe analysis.
 22. The method of claim 21, comprising decompressingconfiguration data before configuring the state machine lattice.
 23. Themethod of claim 21, comprising receiving instructions at an instructionbuffer of the state machine engine.
 24. The method of claim 23,comprising providing the instructions to the lattice programming system.25. The method of claim 24, wherein configuring the state machinelattice using the lattice programming system comprises determiningwhether the instructions are configuration instructions and configuringthe state machine lattice if the instructions are configurationinstructions.